Non-Gaussian Linear Mixing Models for Hyperspectral Images

<table>Base 10 log values of the chi-square statistic (for testing the fit to the scaled <i>t</i>-distribution) plotted versus the dimension number <svg height="15.7" id="M194" style="vertical-align:-3.56265pt" version="1.1" viewbox="0 0 7.3125 15.7" width="7.3125" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,0,0,-.017,.062,11.2)"><path d="M394 607q0 -25 -15.5 -43t-39.5 -18q-19 0 -32 13t-13 35t16 41.5t40 19.5q20 0 32 -13.5t12 -34.5zM360 349l-57 -283q-38 -188 -122 -263q-72 -64 -114 -64q-16 0 -30 10.5t-14 22.5q0 24 21 45q15 13 26 6q30 -20 54 -20q41 0 65 95q37 147 86 415q6 34 6.5 42.5
t-5.5 11.5q-18 0 -79 -54l-14 25q48 48 96.5 79t71.5 31q29 0 9 -99z" id="x1D457"></path></g>
</svg> (for the Cooke City image).</table>

Journal of Electrical and Computer Engineering

fig12

Figure 12

Figure 12: Non-Gaussian Linear Mixing Models for Hyperspectral Images 